A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images

Hao Jiang; Jason Haimerl; Xuejun Gu; Weiguo Lu

doi:10.1007/978-3-031-27420-6_4

A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images

Hao Jiang, Jason Haimerl, Xuejun Gu, Weiguo Lu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Delineation of head and neck lesions are crucial for radiation treatment planning and follow-up studies. In this paper we developed an automated segmentation method for head and neck primary and nodal gross tumor volumes (GTVp and GTVn) segmentation in positron emission tomography/computed tomography (PET/CT) provided by the MICCAI 2022 Head and Neck Tumor Segmentation Challenge (HECKTOR 2022). Our segmentation algorithm takes nnU-Net as the backbone and uses dedicated pre- and post-processing to improve the auto-segmentation performance. The pipeline described achieved DSC results of 0.77212 (GTVp 0.77485 and GTVn 0.76938) in the testing dataset of HECTOR 2022. The developed auto-segmentation method is further extensively developed to a web-based platform to permit easy access and facilitate clinical workflow.

Original language	English (US)
Title of host publication	Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings
Editors	Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge, Mathieu Hatt
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	47-53
Number of pages	7
ISBN (Print)	9783031274190
DOIs	https://doi.org/10.1007/978-3-031-27420-6_4
State	Published - 2023
Event	3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: Sep 22 2022 → Sep 22 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13626 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	9/22/22 → 9/22/22

Keywords

Head and neck cancer
Segmentation
nnU-Net

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-27420-6_4

Cite this

Jiang, H., Haimerl, J., Gu, X., & Lu, W. (2023). A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images. In V. Andrearczyk, V. Oreiller, A. Depeursinge, & M. Hatt (Eds.), Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings (pp. 47-53). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13626 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-27420-6_4

A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images. / Jiang, Hao; Haimerl, Jason; Gu, Xuejun et al.
Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings. ed. / Vincent Andrearczyk; Valentin Oreiller; Adrien Depeursinge; Mathieu Hatt. Springer Science and Business Media Deutschland GmbH, 2023. p. 47-53 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13626 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jiang, H, Haimerl, J, Gu, X & Lu, W 2023, A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images. in V Andrearczyk, V Oreiller, A Depeursinge & M Hatt (eds), Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13626 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 47-53, 3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Singapore, Singapore, 9/22/22. https://doi.org/10.1007/978-3-031-27420-6_4

Jiang H, Haimerl J, Gu X, Lu W. A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images. In Andrearczyk V, Oreiller V, Depeursinge A, Hatt M, editors, Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 47-53. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-27420-6_4

Jiang, Hao ; Haimerl, Jason ; Gu, Xuejun et al. / A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images. Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings. editor / Vincent Andrearczyk ; Valentin Oreiller ; Adrien Depeursinge ; Mathieu Hatt. Springer Science and Business Media Deutschland GmbH, 2023. pp. 47-53 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Delineation of head and neck lesions are crucial for radiation treatment planning and follow-up studies. In this paper we developed an automated segmentation method for head and neck primary and nodal gross tumor volumes (GTVp and GTVn) segmentation in positron emission tomography/computed tomography (PET/CT) provided by the MICCAI 2022 Head and Neck Tumor Segmentation Challenge (HECKTOR 2022). Our segmentation algorithm takes nnU-Net as the backbone and uses dedicated pre- and post-processing to improve the auto-segmentation performance. The pipeline described achieved DSC results of 0.77212 (GTVp 0.77485 and GTVn 0.76938) in the testing dataset of HECTOR 2022. The developed auto-segmentation method is further extensively developed to a web-based platform to permit easy access and facilitate clinical workflow.",

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A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this